A WiFi offloading method based on matching game in synaesthesia integrated network

Abstract

The present invention discloses a matching game-based WiFi offloading method in a synaesthesia-integrated network. This method addresses the problem of simultaneous user communication demands and radar sensing demands in heterogeneous networks, where limited cellular base station channel resources prevent the timely processing of all tasks. WiFi offloading is introduced to reduce the load on the base station by placing some user communication tasks within the WiFi network. This method uses a clustering-based WiFi channel allocation algorithm to allocate channels to WiFi access points sharing an unlicensed frequency band, formulating the user selection problem of WiFi network offloading into a matching game that considers externalities. This method considers the communication throughput and perceptual mutual information in the system, treating users and access points as the two parties in the matching game. Utility functions for users and access points are established, respectively. By designing and optimizing the time slot allocation for sensing and communication in the base station, the stability of the algorithm is demonstrated, achieving optimal performance of the synaesthesia-integrated system.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a WiFi offloading method based on matching game in a synaesthesia integrated network. Background Art

[0002] With the increasing popularity of mobile smart devices, people are becoming more and more dependent on cellular networks. Data-intensive applications such as voice calls, live streaming, and short video sharing are becoming increasingly popular. This brings great convenience to people, but also places a huge burden on cellular networks. Users may experience severe network congestion, especially in airports, stadiums, cinemas, tourist attractions, and other densely populated areas.

[0003] To address the surge in mobile data traffic, Wi-Fi-based traffic offloading has become a popular solution among operators. It not only enables users to offload their traffic anytime and anywhere, but also ensures that users achieve their desired QoS.

[0004] The essence of WiFi offloading is user association. This involves offloading user data traffic to the WiFi network by leveraging high-performance WiFi access points. This improves the service quality of resource-constrained base stations and effectively alleviates network offload pressure. In real-world scenarios, dual functional radar and communication (DFRC) base stations integrate wireless communication and sensing functions by sharing signal processing algorithms and hardware components. Given limited resources, it's necessary to offload some communication tasks to the Wi-Fi network to improve the communication and sensing performance of heterogeneous network systems.

[0005] While traffic offloading offers many advantages, it doesn't necessarily translate to universal acceptance. If users can't profit from the offloading solution, they might not necessarily be willing to offload traffic to other networks. Heterogeneous network systems require well-designed offloading solutions to achieve an inherent balance between communication and perception functions. Summary of the Invention

[0006] Purpose of the invention: To overcome the shortcomings of the existing technology, the purpose of the present invention is to provide a WiFi offloading method based on matching game in a synaesthesia integrated network. When user communication needs and radar perception needs exist simultaneously in a heterogeneous network, the communication tasks of some users are placed on the WiFi network, thereby reducing the load on the base station and optimizing the performance of the synaesthesia integrated system.

[0007] In response to the simultaneous demands of user communication and radar sensing in heterogeneous networks, cellular base stations are unable to process all tasks on time due to limited channel resources. WiFi offloading is introduced to offload some user communication tasks to the WiFi network, thereby reducing the load on the base station.

[0008] A clustering-based WiFi channel allocation algorithm allocates channels to WiFi access points that share unlicensed frequency bands. Users and access points are considered as two parties in a matching game. The communication throughput and perceptual mutual information in the system are considered, and utility functions for users and access points are established respectively. WiFi offloading is used to optimize the allocation of time slots for perception and communication in the base station, thereby optimizing the performance of the inter-sensory integrated system.

[0009] Technical solution:

[0010] In a first aspect, a WiFi offloading method based on matching game in a synaesthesia integrated network is provided, comprising:

[0011] Step (1) In response to the simultaneous presence of user communication needs and radar sensing needs in a heterogeneous network, the following user set is obtained based on the locations of cellular base stations, WiFi access points, and users: Access Point Collection Where s0 represents a cellular base station, {s1,s2,...,s N} represents a WiFi access point. The total number of WiFi access points is N. i ,i∈{1,2,...,N} represents the set of WiFi access points, each access point s i The upper limit of access users is z i ;

[0012] Step (2) uses the clustering-based WiFi channel allocation algorithm to allocate WiFi access points {s1, s2, ..., s N Allocate unlicensed channels

[0013] Step (3) Calculate the utility function U of each user k UE (k) and sort them in descending order to get the preference list of user k for access points

[0014] Step (4) Each user k is assigned a preference list. The ranking sends an access request to the access point;

[0015] Step (5) If the access point has reached the access limit, it sends an access request to the next access point until it is s i Accept and get matching pair (k,s i );

[0016] Step (6) The access point associated with the current user k is s i , for each access to s j,j≠i User k', if the exchange matches If it can happen, update the matching results

[0017] Step (7) For each matching pair (k,s i ) of user k and each s j,j≠i , if the swap matches Can happen and the access point has not reached the access limit, update the matching result

[0018] Step (8) repeats the above steps (6) and (7) until no exchange matching can occur in the system, and outputs the final matching result;

[0019] Step (9) offloads the user communication task and radar perception task according to the final matching result.

[0020] In some embodiments, step (2) includes:

[0021] Based on available channels Divide the WiFi access points in the network into M clusters, using Represents a set of clusters, using Indicates AP i The sum of the interference from other WiFi access points; Represents cluster C m Before joining AP i After that, other users in the cluster receive the i The sum of the interferences;

[0022] (21) Initialization: {w i} i∈{AP} =0,M;

[0023] (22) For the set {V} of WiFi access points, calculate each element v i The sum of the edge weights received from other WiFi networks w i ;

[0024] (23) Compare {w i} i∈{AP} The size of the WiFi access points is sorted in descending order, and the WiFi access points are renumbered in the sorted order to obtain a new set {V'};

[0025] (24) Take the first M WiFi access points in {V'} and add them to M clusters in order;

[0026] (25) Starting from the M+1th WiFi access point in {V'}, that is, when M+1<i≤N, calculate the AP i When it joins each cluster, the sum of the interference weights on the existing WiFi access points in the cluster is {W i m},m∈{1,2,...,M};AP i Select W i m The smallest cluster is joined;

[0027] (26) All WiFi access points select a cluster to join and assign a channel to each cluster.

[0028] In some embodiments, in step (3), the method for calculating the utility function of each user includes:

[0029]

[0030] Among them, I rad is the mutual information of radar perception in a unit time slot; δ is the economic compensation coefficient, δI rad It indicates the financial compensation given by the network operator to the user when the user offloads to the WiFi network; U UE (k,s i ) indicates that user k accesses access point s i The utility function of the user is They represent the throughput of user k accessing the cellular base station and the WiFi access point respectively.

[0031] Furthermore, the throughput of user k accessing the cellular base station is The calculation methods include:

[0032]

[0033] when hour,

[0034]

[0035] Among them, B CE is the channel bandwidth of the cellular base station, P CE is the transmit power of the cellular base station, is the channel gain from the cellular base station to user k, N0 is the Gaussian white noise power spectral density of the system; the cellular base station channel is divided into L time slots; time slot l is one of the time slots; the time slot allocation matrix A is an L×(K+1) matrix, with rows representing time slot blocks and columns representing access terminals; when When A(l,k)=1, it means that time slot l is allocated to user k, and A(l,k)=0 means that the user does not occupy this time slot.

[0036] Furthermore, the throughput of user k accessing the WiFi access point is The calculation methods include:

[0037]

[0038] Among them, e m,j Is a binary indicator function, indicating the WiFi access point AP j For the occupancy of channel m, when e m,j =1, indicating AP j Use channel m for data transmission, when e m,j =0, indicating AP j Channel m is not used;

[0039] N_AP i Indicates access to a WiFi access point AP i The total number of users, B AP is the channel bandwidth of the WiFi access point, P AP is the transmission power of the WiFi access point, h k,j WiFi access point AP j The channel gain to user k, N0 is the Gaussian white noise power spectral density of the system; the total number of WiFi access points is N.

[0040] The conditions for exchange matching in steps (6) and (7) to occur are:

[0041] For the users and access points involved in the exchange matching, the sum of the utilities after the exchange is greater than the utility before the exchange, and the utility of each user and access point after the exchange is greater than or equal to the utility before the exchange.

[0042] In some embodiments, step (6) includes:

[0043] Define μ as the matching result between the user and the access point in the current state, (k,s i ), (k',s j ) are two matching pairs, that is, they satisfy: k∈μ(s i ), k'∈μ(s j );definition For an exchange match, The conditions for users k and k' to exchange and match are:

[0044]

[0045]

[0046] in, Indicates that in the new matching pair (k,s j ),(k',s i ) condition, user k, k' and access point s i ,s j The utility of U x (μ) represents the user k, k' and access point s under the original matching conditions i ,s j The utility of

[0047] Indicates any Indicates existence and are all mathematical symbols.

[0048] In some embodiments, step (7) includes:

[0049] Define μ as the matching result between the user and the access point in the current state. For the matching pair (k,s i ) and access points j , define the exchange match User k is re-matched to access point s j If and only if:

[0050]

[0051]

[0052] in, Represents a new matching pair (k,s j ) condition, user k and access point s i ,s j The utility of U x (μ) represents the original matching conditions between user k and access point s i ,s j The utility of

[0053] Indicates any Indicates existence and are all mathematical symbols.

[0054] In some embodiments, a method for calculating the utility of an access point includes:

[0055] The utility of a cellular base station is a fixed value;

[0056] WiFi Access Point AP i The utility function is expressed as:

[0057]

[0058] in is the throughput of user k accessing the WiFi access point.

[0059] In a second aspect, the present invention provides a WiFi offloading device based on matching game in a synaesthesia integrated network, comprising a processor and a storage medium;

[0060] The storage medium is used to store instructions;

[0061] The processor is configured to operate according to the instructions to execute the steps of the method according to the first aspect.

[0062] In a third aspect, the present invention provides a storage medium having a computer program stored thereon, which implements the steps of the method described in the first aspect when executed by a processor.

[0063] Beneficial effects:

[0064] Compared with the prior art, the present invention has the following significant advantages:

[0065] 1. The method of the present invention aims to maximize the amount of perceptual mutual information and communication throughput in the system, and designs and optimizes the time slot allocation for perception and communication in the base station.

[0066] 2. The user selection problem of Wi-Fi network offloading is formulated as a matching game that takes externalities into account. The user and access point are considered the two parties in the matching game, and their utility functions are established. A stable match is achieved when no user in the system is willing to exchange matches with any other user or rematch with another access point.

[0067] 3. Unlike traditional resource allocation in heterogeneous networks, the method of the present invention introduces interawareness integration into the WiFi offloading process, combined with the economic compensation given by network operators to users, to encourage users to participate in offloading, effectively improving perception performance and reducing the load on base stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 Schematic diagram of a process of an embodiment of the present invention;

[0069] Figure 2 A schematic diagram of a system model of a method according to an embodiment of the present invention; DETAILED DESCRIPTION

[0070] The present invention is further described in detail below with reference to the accompanying drawings and embodiments.

[0071] In the description of the present invention, "several" means more than one, "plurality" means more than two, "greater than," "less than," and "exceed" are understood to exclude the number itself, while "above," "below," and "within" are understood to include the number itself. The use of "first" and "second" in the description is solely for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance, implicitly specifying the number of the indicated technical features, or implicitly specifying the order of the indicated technical features.

[0072] In the description of the present invention, reference to terms such as "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the exemplary expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0073] Example 1

[0074] like Figure 1 As shown, a WiFi offloading method based on matching game in a synaesthesia integrated network includes:

[0075] Step (1) In response to the simultaneous presence of user communication needs and radar sensing needs in a heterogeneous network, the following user set is obtained based on the locations of cellular base stations, WiFi access points, and users: Access Point Collection Where s0 represents a cellular base station, {s1,s2,...,s N} represents a WiFi access point. The total number of WiFi access points is N. i ,i∈{1,2,...,N} represents the set of WiFi access points, each access point s i The upper limit of access users is z i ;

[0076] Step (2) uses the clustering-based WiFi channel allocation algorithm to allocate WiFi access points {s1, s2, ..., s N Allocate unlicensed channels

[0077] Step (3) Calculate the utility function U of each user k UE (k) and sort them in descending order to get the preference list of user k for access points

[0078] Step (4) Each user k is assigned a preference list. The ranking sends an access request to the access point;

[0079] Step (5) If the access point has reached the access limit, it sends an access request to the next access point until it is s i Accept and get matching pair (k,s i );

[0080] Step (6) The access point associated with the current user k is s i , for each access to s j,j≠i User k', if the exchange matches If it can happen, update the matching results

[0081] Step (7) For each matching pair (k,s i ) of user k and each s j,j≠i , if the swap matches Can happen and the access point has not reached the access limit, update the matching result

[0082] Step (8) repeats the above steps (6) and (7) until no exchange matching can occur in the system, and outputs the final matching result;

[0083] Step (9) offloads the user communication task and radar perception task according to the final matching result.

[0084] The system model of the present invention is as follows Figure 2 As shown, the system consists of a dual-function millimeter-wave cellular base station, several Wi-Fi access points, a group of users, and detection targets. The base station is equipped with integrated communication and sensing technology, enabling both communication and sensing. Because the base station has limited backhaul and radio access capacity, to better facilitate communication and sensing, the base station encourages some users within the Wi-Fi access point's coverage area to connect to the Wi-Fi network, thereby improving overall network performance.

[0085] use To represent the users in the system, there are K users in total. Cellular base stations use licensed frequency bands to provide services to users, while WiFi networks use unlicensed frequency bands, so there is no interference between the two. Considering that the base station performs radar sensing tasks and accesses user communication services in a time-division structure, the channel is divided into L time slots, using express.

[0086] The slot allocation matrix A is used to represent the slot occupancy of the cellular base station. Communication and sensing functions cannot coexist in a single slot. The slot allocation matrix A is an L×(K+1) matrix, where A(l,k) is a binary variable, the rows represent the slot blocks, and the columns represent the terminals connected. When A(l,k) = 1, time slot l is assigned to user k, and A(l,k) = 0 indicates that the user does not occupy this time slot. Specifically, let k = K + 1 represent the radar sensing task. When A(l,K + 1) = 1, the base station occupies time slot l for radar detection. Each cellular user is limited to one time slot for data transmission. Network operators prioritize the communication needs of cellular users. After allocating time slots to all users connected to the base station, radar sensing can occupy multiple time slots. Therefore,

[0087]

[0088] The throughput of user k can be expressed as:

[0089]

[0090] Among them, B CE is the channel bandwidth of the cellular base station, P CE is the transmit power, is the channel gain from the base station to user k, and N0 is the Gaussian white noise power spectral density of the system.

[0091] For radar perception, the perception signal received by the base station can be expressed as:

[0092] y rad =x rad g rad +c+n0 (3)

[0093] Among them, x rad Indicates sending a signal, y rad Indicates the radar receiving signal, g rad It is expressed as the transmission gain of the radar response signal in the channel, c and n0 respectively represent the mean of 0 and the variance of The clutter in the environment includes the reflected signal from the user and Gaussian white noise with a power spectral density of N0. Considering that millimeter waves have good beam directivity, the antenna gain is defined as the main lobe gain, and its corresponding transmission path gain is:

[0094]

[0095] Among them, G t is the radar transmitting antenna gain, G r is the radar receiving antenna gain, σ RCS is the effective radar cross section of the target relative to the radar, λ is the wavelength, and d is the distance from the target to the radar. The radar signal-to-interference-noise ratio can be expressed as:

[0096]

[0097] To facilitate the measurement of perception and communication performance, radar mutual information is used to evaluate the perception capabilities of cellular base stations. Similar to the mutual information (I(X; Y)) in a communication system, which represents the amount of information about X contained in Y, radar mutual information represents the amount of information about the channel state contained in the radar's received signal. Related literature indicates that maximizing mutual information (MI) can effectively enhance a radar system's target detection capabilities. Therefore, representing radar mutual information as the MI between the target's response and the received signal, the radar mutual information per unit time slot can be expressed as:

[0098] I rad =B CE log(1+γ rad ) (6)

[0099] The radar mutual information in the entire radar perception process can be expressed as:

[0100]

[0101] Since WiFi access points use the contention access mechanism of Carrier Sense Multiple Access with Collision Avoidance (CSMA / CA), each user accessing WiFi will monopolize the entire bandwidth of the WiFi access point within a certain period of time. Assume that the total number of WiFi access points is N, and the number of APs is i ,i∈{1,2,...,N} represents the set of WiFi access points, then AP i The total working time can be expressed as:

[0102]

[0103] Among them, t i,k The kth user connects to the AP i The duration of receiving data.

[0104] use Represents the available WiFi channels in the system. There are M channels in total, and each channel has the same bandwidth B AP , each WiFi access point occupies a channel to work. Since the number of WiFi access points is greater than the number of available channels, it is necessary to reuse channels to serve users. Therefore, there is interference between WiFi networks working on the same channel. Assume that AP i Using channel m, User k connects to AP i , then the interference and noise suffered by user k can be expressed as:

[0105]

[0106] Among them, e m,j is a binary indicator function, indicating AP j For the occupancy of channel m, when e m,j =1, indicating AP j Use channel m for data transmission, when e m,j =0, indicating AP j Channel m is not used. AP is the transmission power of the WiFi access point, h k,j For AP j The channel gain to user k, N0 is the system’s Gaussian white noise power spectrum density. Assume that access AP i The total number of users is N_AP i , and the time resources are evenly shared among users accessing the same WiFi, so there is Therefore, the throughput of user k accessing the WiFi access point can be expressed as:

[0107]

[0108] The goal of this paper is to maximize communication throughput and perceptual mutual information in heterogeneous network systems. Considering actual performance, user throughput is constrained to ensure minimum performance requirements. α and β represent the weight coefficients for communication and perception, respectively, and b[k]∈{0,1} is a binary selection variable expressed as:

[0109]

[0110] Therefore, the optimization problem is modeled as:

[0111]

[0112] Because Wi-Fi networks share unlicensed frequency bands and are irregularly distributed, a clustering-based Wi-Fi channel allocation algorithm can be used to allocate channels to each Wi-Fi access point to reduce interference between Wi-Fi networks operating on the same channel, thereby improving Wi-Fi network performance.

[0113] First, a directed weighted graph G = (V, E, W) of the WiFi network is established, where {V} = {v1, v2, ..., v N} is the set of vertices in the weighted graph, i.e., the set of WiFi access points, {E}={e n,i} n,i∈{AP} is the set of edges in the weighted graph, if e n,i =1, indicating AP n AP i Generate interference, otherwise e n,i =0 means AP n AP iNo interference occurs. {W}={w n,i} n,i∈{AP} is the edge weight correlation function, namely AP n AP i The size of the interference can be written as:

[0114] w n,i =max(G n,i -δ,0) (13)

[0115] Among them, G n,i Indicates AP n AP i The channel gain of G is δ, and the receiving sensitivity of each WiFi access point is δ. n,i When AP is greater than sensitivity δ, n AP i There is interference and the weight is G n,i -δ; when G n,i When w is less than the sensitivity δ, n,i =0,AP n AP i There is no interference.

[0116] Use the clustering idea to allocate unlicensed frequency band channels to each WiFi network, and Divide the WiFi access points in the network into M clusters, using Represents a set of clusters, using Indicates AP i The sum of the interference received from other WiFi access points. Represents cluster C m Before joining AP i After that, other users in the cluster receive the i The sum of the interference.

[0117] (21) Initialization: {w i} i∈{AP} =0,M;

[0118] (22) For the set {V} of WiFi access points, calculate each element v i The sum of the edge weights received from other WiFi networks w i ;

[0119] (23) Compare {w i} i∈{AP} The size of the WiFi access points is sorted in descending order, and the WiFi access points are renumbered in the sorted order to obtain a new set {V'};

[0120] (24) Take the first M WiFi access points in {V'} and add them to M clusters in order;

[0121] (25) Starting from the M+1th WiFi access point in {V'}, that is, when M+1<i≤N, calculate the AP i When it joins each cluster, the sum of the interference weights on the existing WiFi access points in the cluster is {W i m},m∈{1,2,...,M}.AP i Select W i m The smallest cluster is joined;

[0122] (26) All WiFi access points select clusters to join, a channel is assigned to each cluster, and the algorithm ends.

[0123] Matching theory can account for the selfishness and rationality of different participants, allowing them to define their own preferences. Given the importance of the association between users and access points to the overall network throughput and perception function, a matching game is introduced to consider the association between users and access points from both the network and user perspectives, ultimately achieving a good balance between communication and perception functions in the system.

[0124] The association problem between users and access points can be reformulated as a bilateral many-to-one matching problem with externalities. Here are some properties of matching game theory:

[0125] (1) Participants: Participants maximize their own interests by implementing strategies in the matching game. In this invention, the two matching parties are users and access points. The set of users is represented as The access point set consists of a group of WiFi access points and a cellular base station. Indicates that s0 represents a cellular base station, {s1,s2,...,s N} represents a collection of WiFi access points.

[0126] (2) Preference relationship: represents the user's preference relationship between WiFi access points and cellular base stations, represents the preference relationship between WiFi access points and cellular base stations for users. i and s j ,have s i ≠s j , s i > k s j , which means that user k is more inclined to choose to access s i Instead of s jSimilarly, for any two users k and k', we have k≠k', , then for access point s i , compared with user k', user k is preferred to access.

[0127] (3) Policy set: Each user selects a network to access based on his or her preferences; each access point s i ,i∈{0,1,2,...,N} is at most equal to z i users are matched.

[0128] (4) Utility function: The preference relationship is quantified using a mathematical model to obtain the priority of the preference relationship. For selfish reasons, the user's utility function consists of the throughput obtained when accessing the base station or WiFi, and the economic compensation given by the network operator when offloading to the WiFi access point. The utility function of user k is expressed as:

[0129]

[0130] Among them, I rad is the mutual information of radar perception in a unit time slot; δI rad This refers to the financial compensation that network operators give users when they offload traffic to WiFi networks. When users connect to WiFi networks, cellular base stations have more idle time slots for radar sensing. To encourage more users to offload traffic, base stations offer users corresponding compensation, which can be in the form of data discounts.

[0131] The throughput obtained by users accessing the WiFi network is used as the utility function, then AP i The utility function is expressed as:

[0132]

[0133] The quota of the base station and the number of user matches that it can accept are greater than or equal to the total number of users. Therefore, there is no need to set a preference function for the base station at this time because the base station can always accommodate all users.

[0134] By calculating the utility function according to the above formula and sorting them in descending order, we can get the user's preference list for base stations and WiFi access points respectively. and access point preference lists for users This gives the definition of the bilateral matching game:

[0135] Definition 1 (Bilateral Matching): A bilateral matching Define the matching μ as Map to A function if and only if:

[0136] (1)

[0137] (2)

[0138] (3)μ(k)=s i , if and only if s i ∈μ(k) is established

[0139] Condition (1) means that each user can only select one small base station to access at the same time; Condition (2) means that each access point s i The upper limit of access users is z i ; Condition (3) means that when user k and access point s i When associated, this access point must also match the user.

[0140] The utility functions of users and Wi-Fi access points show that users compete for Wi-Fi network access time. Therefore, a user's utility depends not only on the Wi-Fi access point it matches but also on the set of users associated with the same Wi-Fi access point. In matching games, this dependence on the strategy choices of other participants is called an externality.

[0141] In a bidirectional matching problem with externalities, the preference lists of users and Wi-Fi access points change dynamically as the user access matching pairs change. Therefore, the traditional delayed acceptance algorithm is no longer applicable to this problem. Instead, the concept of exchange matching is introduced and defined as follows:

[0142] The conditions for exchange matching in steps (6) and (7) to occur are:

[0143] For the users and access points involved in the exchange matching, the sum of the utilities after the exchange is greater than the utility before the exchange, and the utility of each user and access point after the exchange is greater than or equal to the utility before the exchange.

[0144] Definition 2 (Exchange Matching): (1) Define μ as the matching result between the user and the access point in the current state, (k,s i ), (k',s j ) are two matching pairs, that is, they satisfy: k∈μ(s i ), k'∈μ(s j ).definition For an exchange match, Users k and k' can be exchanged and matched if and only if:

[0145]

[0146] (2) For the matching pair (k,s i ) and access pointsj , define the exchange match User k is re-matched to access point s j If and only if:

[0147]

[0148] The above definition indicates that an exchange matching occurs if and only if the sum of the utility functions of the users and access points involved is greater than 0.

[0149] The following is the definition of stability:

[0150] Definition 3 (Stability of Matching Game): A matching μ is bilaterally stable if and only if there is no possible exchange matching in the matching μ. That is, for any matching pair (k,s i ), user k is unwilling to exchange matches with any other user, or re-match with other access points. At this time, we say that the matching μ is stable.

[0151] This paper proposes a matching game algorithm that takes externalities into account. Users are first matched with access points according to an initial preference list. Then, according to the definition of exchange matching, the user's access situation is changed to achieve better utility for both matching parties until the matching reaches a stable state.

[0152] In summary, the present invention discloses a WiFi offloading method based on matching game in a synaesthesia integrated network. This method addresses the problem that when user communication needs and radar perception needs exist simultaneously in a heterogeneous network, due to the limited channel resources of the cellular base station, all tasks cannot be processed on time. WiFi offloading is introduced to place the communication tasks of some users on the WiFi network to reduce the load on the base station. The method uses a clustering-based WiFi channel allocation algorithm to allocate channels to WiFi access points that share unlicensed frequency bands, and establishes the offloading problem of user selection of WiFi networks as a matching game considering externalities. The method considers the communication throughput and the amount of perception mutual information in the system, takes users and access points as the two parties in the matching game, establishes the utility functions of users and access points respectively, and proves the stability of the algorithm by designing and optimizing the time slot allocation for perception and communication in the base station, thereby achieving the optimization of the performance of the synaesthesia integrated system.

[0153] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0154] Example 2

[0155] In a second aspect, this embodiment provides a WiFi offloading device based on matching game in a synaesthesia integrated network, including a processor and a storage medium;

[0156] The storage medium is used to store instructions;

[0157] The processor is configured to operate according to the instructions to execute the steps of the method according to embodiment 1.

[0158] Example 3

[0159] In a third aspect, this embodiment provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in Example 1 are implemented.

[0160] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0161] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0162] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0163] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0164] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A WiFi offloading method based on matching game in a synaesthesia integrated network, characterized in that: include: Step (1) In response to the simultaneous presence of user communication needs and radar sensing needs in a heterogeneous network, the following user set is obtained based on the locations of cellular base stations, WiFi access points, and users: Access Point Collection Where s0 represents a cellular base station, {s1,s2,…,s N } represents a WiFi access point. The total number of WiFi access points is N. i ,i∈{1,2,…,N} represents the set of WiFi access points, each access point s i The upper limit of access users is z i ; Step (2) uses the clustering-based WiFi channel allocation algorithm to allocate WiFi access points {s1, s2, ..., s N Allocate unlicensed channels Step (3) Calculate the utility function U of each user k UE (k) and sort them in descending order to get the preference list of user k for access points The utility function calculation method for each user includes: Among them, I rad is the mutual information of radar perception in a unit time slot; δ is the economic compensation coefficient, δI rad It indicates the financial compensation given by the network operator to the user when the user offloads to the WiFi network; U UE (k,s i ) indicates that user k accesses access point s i The utility function of the user is They represent the throughput of user k accessing the cellular base station and the WiFi access point respectively; Step (4) Each user k is assigned a preference list. The ranking sends an access request to the access point; Step (5) If the access point has reached the access limit, it sends an access request to the next access point until it is s i Accept and get matching pair (k,s i ); Step (6) The access point associated with the current user k is s i , for each access to s j,j≠i User k', if the exchange matches If it can happen, update the matching results Step (7) For each matching pair (k,s i ) of user k and each s j,j≠i , if the swap matches Can happen and the access point has not reached the access limit, update the matching result Step (8) repeats the above steps (6) and (7) until no exchange matching can occur in the system, and outputs the final matching result; Step (9) offloads the user communication task and radar perception task according to the final matching result.

2. The WiFi offloading method based on matching game in the synaesthesia integrated network according to claim 1 is characterized in that: The step (2) comprises: Based on available channels Divide the WiFi access points in the network into M clusters, using Represents a set of clusters, using Indicates AP i The sum of the interference from other WiFi access points; Represents cluster C m Before joining AP i After that, other users in the cluster receive the i The sum of the interferences; (21) Initialization: (22) For the set {V} of WiFi access points, calculate each element v i The sum of the edge weights received from other WiFi networks w i ; (23) Compare {w i } i∈{AP} The size of , and arrange them in descending order, renumber the WiFi access points in the sorted order to obtain a new set {V′}; (24) Take the first M WiFi access points in {V′} and add them to M clusters in order; (25) Starting from the (M + 1)-th WiFi access point in {V'}, that is, when M + 1 < i ≤ N, calculate respectively when the AP i When it joins each cluster, the sum of the interference weights on the existing WiFi access points within the cluster, that is AP i Select the cluster with the minimum value and join it; (26) All WiFi access points select a cluster to join and assign a channel to each cluster.

3. The WiFi offloading method based on matching game in the synaesthesia integrated network according to claim 1 is characterized in that: The throughput of user k accessing the cellular base station The calculation methods include: when hour, Among them, B CE is the channel bandwidth of the cellular base station, P CE is the transmit power of the cellular base station, is the channel gain from the cellular base station to user k, N0 is the Gaussian white noise power spectral density of the system; the cellular base station channel is divided into L time slots; time slot l is one of the time slots; the time slot allocation matrix A is an L×(K+1) matrix, with rows representing time slot blocks and columns representing access terminals; when When A(l,k)=1, it means that time slot l is allocated to user k, and A(l,k)=0 means that the user does not occupy this time slot.

4. The WiFi offloading method based on matching game in the synaesthesia integrated network according to claim 1 is characterized in that: Throughput of user k accessing the WiFi access point The calculation methods include: Among them, e m,j Is a binary indicator function, indicating the WiFi access point AP j For the occupancy of channel m, when e m,j =1, indicating AP j Use channel m for data transmission, when e m,j =0, indicating AP j Channel m is not used; N_AP i Indicates access to a WiFi access point AP i The total number of users, B AP is the channel bandwidth of the WiFi access point, P AP is the transmission power of the WiFi access point, h k,j WiFi access point AP j The channel gain to user k, N0 is the Gaussian white noise power spectral density of the system; the total number of WiFi access points is N.

5. The WiFi offloading method based on matching game in the synaesthesia integrated network according to claim 1 is characterized in that: Step (6) includes: Define μ as the matching result between the user and the access point in the current state, (k,s i ), (k′,s j ) are two matching pairs, that is, they satisfy: k∈μ(s i ), k′∈μ(s j );definition For an exchange match, The conditions for users k and k′ to exchange and match are: in, Indicates that in the new matching pair (k,s j ),(k′,s i ) condition, user k, k′ and access point s i ,s j The utility of U x (μ) represents the user k, k′ and access point s under the original matching conditions i ,s j The utility of Indicates any Indicates existence and are all mathematical symbols.

6. The WiFi offloading method based on matching game in the synaesthesia integrated network according to claim 1 is characterized in that: Step (7) includes: Define μ as the matching result between the user and the access point in the current state. For the matching pair (k,s i ) and access points j , define the exchange match User k is re-matched to access point s j If and only if: in, Represents a new matching pair (k,s j ) condition, user k and access point s i ,s j The utility of U x (μ) represents the original matching conditions between user k and access point s i ,s j The utility of Indicates any Indicates existence and are all mathematical symbols.

7. The WiFi offloading method based on matching game in the synaesthesia integrated network according to claim 5 or 6, characterized in that: The calculation method of the access point's utility includes: The utility of a cellular base station is a fixed value; WiFi Access Point AP i The utility function is expressed as: in is the throughput of user k accessing the WiFi access point.

8. A WiFi offloading device based on matching game in a synaesthesia integrated network, characterized in that: including processors and storage media; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 7.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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